Performance of the SAEM and FOCEI Algorithms in the Open‐Source, Nonlinear Mixed Effect Modeling Tool nlmixr

The free and open‐source package nlmixr implements pharmacometric nonlinear mixed effects model parameter estimation in R. It provides a uniform language to define pharmacometric models using ordinary differential equations. Performances of the stochastic approximation expectation‐maximization (SAEM...

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Main Authors: Rik Schoemaker (Author), Matthew Fidler (Author), Christian Laveille (Author), Justin J. Wilkins (Author), Richard Hooijmaijers (Author), Teun M. Post (Author), Mirjam N. Trame (Author), Yuan Xiong (Author), Wenping Wang (Author)
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Published: Wiley, 2019-12-01T00:00:00Z.
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100 1 0 |a Rik Schoemaker  |e author 
700 1 0 |a Matthew Fidler  |e author 
700 1 0 |a Christian Laveille  |e author 
700 1 0 |a Justin J. Wilkins  |e author 
700 1 0 |a Richard Hooijmaijers  |e author 
700 1 0 |a Teun M. Post  |e author 
700 1 0 |a Mirjam N. Trame  |e author 
700 1 0 |a Yuan Xiong  |e author 
700 1 0 |a Wenping Wang  |e author 
245 0 0 |a Performance of the SAEM and FOCEI Algorithms in the Open‐Source, Nonlinear Mixed Effect Modeling Tool nlmixr 
260 |b Wiley,   |c 2019-12-01T00:00:00Z. 
500 |a 2163-8306 
500 |a 10.1002/psp4.12471 
520 |a The free and open‐source package nlmixr implements pharmacometric nonlinear mixed effects model parameter estimation in R. It provides a uniform language to define pharmacometric models using ordinary differential equations. Performances of the stochastic approximation expectation‐maximization (SAEM) and first order‐conditional estimation with interaction (FOCEI) algorithms in nlmixr were compared with those found in the industry standards, Monolix and NONMEM, using the following two scenarios: a simple model fit to 500 sparsely sampled data sets and a range of more complex compartmental models with linear and nonlinear clearance fit to data sets with rich sampling. Estimation results obtained from nlmixr for FOCEI and SAEM matched the corresponding output from NONMEM/FOCEI and Monolix/SAEM closely both in terms of parameter estimates and associated standard errors. These results indicate that nlmixr may provide a viable alternative to existing tools for pharmacometric parameter estimation. 
546 |a EN 
690 |a Therapeutics. Pharmacology 
690 |a RM1-950 
655 7 |a article  |2 local 
786 0 |n CPT: Pharmacometrics & Systems Pharmacology, Vol 8, Iss 12, Pp 923-930 (2019) 
787 0 |n https://doi.org/10.1002/psp4.12471 
787 0 |n https://doaj.org/toc/2163-8306 
856 4 1 |u https://doaj.org/article/2e1c6e4ff2d84c399b05ad5b72e6c0d5  |z Connect to this object online.